Operations | Monitoring | ITSM | DevOps | Cloud

May 2021

Ways AI is Driving More Efficient Application Performance Monitoring

In the digital age, the speed and performance of apps and websites have a huge impact on the customer experience. To ensure a high level of quality, Application Performance Monitoring (APM) refers to the process of tracking the performance and availability of software systems. Let’s look at what Application Performance Monitoring is, how AI and machine learning are being applied to stay ahead of the competition, and several real-world use cases.

Automated Anomaly Detection: The next step for CSPs

Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks, in a competitive and unforgiving market with minimal time to resolution and high costs for errors. With the ongoing growth in operational complexities, effectively managing radio networks, current and legacy core networks, services, and transport and IT operations is becoming a radical challenge.

Anodot Helps CSPs Jump-Start Zero-Touch Network Monitoring

Anodot’s autonomous network monitoring platform provides the ability to monitor cross-layer network performance and service experience in one platform. We collect all data types, at any scale, and use AI/ML to correlate anomalies across the entire telco stack. Our platform is the "brain" on top of the OSS that detects service-impacting incidents in real time. We help customers like T-Mobile and Megafon protect their revenue and improve service experience - reducing the number of alerts by 90% and shortening Time-to-Resolve incidents by 30%.